





"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


__UpperCamelCase     :						Optional[int]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						str						       =  {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__UpperCamelCase     :						str						       =  {
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

__UpperCamelCase     :						Optional[Any]						       =  {
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Any			):
 with open(_UpperCAmelCase  ,     'r'  ,     encoding='utf-8'			) as f:
  lowerCAmelCase    				= json.loads(f.read()			)

 lowerCAmelCase    				= collections.OrderedDict()
 lowerCAmelCase    				= collections.OrderedDict()
 lowerCAmelCase    				= collections.OrderedDict()
 with open(_UpperCAmelCase  ,     'r'  ,     encoding='utf-8'			) as f:
  lowerCAmelCase    				= f.readlines()
 lowerCAmelCase    				= [[t.rstrip('\n'			)] if (t == ',' or ',' not in t) else t.rstrip('\n'			).split(','			) for t in token]
 for idx, b in enumerate(_UpperCAmelCase			):
  lowerCAmelCase    				= b
  lowerCAmelCase    				= idx
  for wd in b:
   lowerCAmelCase    				= idx

 return vocab, raw_vocab, ids_to_tokens, emoji







class       a							(   a__  ):
 snake_case__             =       VOCAB_FILES_NAMES
 snake_case__             =       PRETRAINED_VOCAB_FILES_MAP
 snake_case__             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case__             =       ['''input_ids''', '''attention_mask''']



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case="<|endoftext|>"				,       _snake_case="<|endoftext|>"				,       _snake_case="<|startoftext|>"				,       _snake_case="<|endoftext|>"				,       _snake_case=False				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(
      unk_token=_snake_case				,       pad_token=_snake_case				,       bos_token=_snake_case				,       eos_token=_snake_case				,       do_clean_text=_snake_case				,       **_snake_case				,       )
  if not os.path.isfile(_snake_case     ):
   raise ValueError(
       F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
       ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'     )
  if not os.path.isfile(_snake_case     ):
   raise ValueError(
       F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
       ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'     )
  lowerCAmelCase    				= do_clean_text
  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= load_vocab_and_emoji(_snake_case				,       _snake_case     )
  lowerCAmelCase    				= SubWordJapaneseTokenizer(
      vocab=self.vocab				,       ids_to_tokens=self.ids_to_tokens				,       emoji=self.emoji     )



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return len(self.raw_vocab     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return dict(self.raw_vocab				,       **self.added_tokens_encoder     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.subword_tokenizer.tokenize(_snake_case				,       clean=self.do_clean_text     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.vocab.get(_snake_case				,       self.vocab.get(self.unk_token     )     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.subword_tokenizer.convert_id_to_token(_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= ''.join(_snake_case     ).strip()
  return out_string



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(_snake_case				,       add_special_tokens=_snake_case     ) + [self.eos_token_id]     )

  if len(_snake_case     ) > self.model_max_length:
   lowerCAmelCase    				= input_ids[-self.model_max_length :]
  return input_ids



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= 0
  if os.path.isdir(_snake_case     ):
   lowerCAmelCase    				= os.path.join(
       _snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
   lowerCAmelCase    				= os.path.join(
       _snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']     )
  else:
   lowerCAmelCase    				= (
       (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
   )
   lowerCAmelCase    				= (
       (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
   )
  with open(_snake_case				,       'w'				,       encoding='utf-8'     ) as writer:
   for token_index, token in self.ids_to_tokens.items():
    if index != token_index:
     logger.warning(
         F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
         ' Please check that the vocabulary is not corrupted!'     )
     lowerCAmelCase    				= token_index
    writer.write(','.join(_snake_case     ) + '\n'     )
    index += 1
  with open(_snake_case				,       'w'				,       encoding='utf-8'     ) as writer:
   json.dump(self.emoji				,       _snake_case     )
  return vocab_file, emoji_file







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vocab  # same as swe
  lowerCAmelCase    				= ids_to_tokens  # same as bpe
  lowerCAmelCase    				= emoji
  lowerCAmelCase    				= np.max([len(_snake_case     ) for w in self.vocab.keys()]     )
  lowerCAmelCase    				= re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'     )
  lowerCAmelCase    				= re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'     )
  lowerCAmelCase    				= re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'     )
  lowerCAmelCase    				= re.compile(
      r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'     )
  lowerCAmelCase    				= re.compile(
      r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'     )
  lowerCAmelCase    				= re.compile(
      r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'     )
  lowerCAmelCase    				= '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
  lowerCAmelCase    				= '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
  lowerCAmelCase    				= str.maketrans({k: '<BLOCK>' for k in keisen + blocks}     )



 def __len__(     self     ):







  """simple docstring"""






  return len(self.ids_to_tokens     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.content_repattera.sub('<URL>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<EMAIL>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<TEL>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<DATE>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<DATE>'				,       _snake_case     )
  lowerCAmelCase    				= self.content_repattera.sub('<PRICE>'				,       _snake_case     )
  lowerCAmelCase    				= content.translate(self.content_transa     )
  while "<BLOCK><BLOCK>" in content:
   lowerCAmelCase    				= content.replace('<BLOCK><BLOCK>'				,       '<BLOCK>'     )
  return content



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=False     ):







  """simple docstring"""






  lowerCAmelCase    				= text.replace(' '				,       '<SP>'     )
  lowerCAmelCase    				= text.replace('　'				,       '<SP>'     )
  lowerCAmelCase    				= text.replace('\r\n'				,       '<BR>'     )
  lowerCAmelCase    				= text.replace('\n'				,       '<BR>'     )
  lowerCAmelCase    				= text.replace('\r'				,       '<BR>'     )
  lowerCAmelCase    				= text.replace('\t'				,       '<TAB>'     )
  lowerCAmelCase    				= text.replace('—'				,       'ー'     )
  lowerCAmelCase    				= text.replace('−'				,       'ー'     )
  for k, v in self.emoji["emoji"].items():
   if k in text:
    lowerCAmelCase    				= text.replace(_snake_case				,       _snake_case     )
  if clean:
   lowerCAmelCase    				= self.clean_text(_snake_case     )

  def check_simbol(_snake_case     ):
   lowerCAmelCase    				= x.encode()
   if len(_snake_case     ) == 1 and len(_snake_case     ) == 2:
    lowerCAmelCase    				= (int(e[0]     ) << 8) + int(e[1]     )
    if (
        (c >= 0xc_2a1 and c <= 0xc_2bf)
        or (c >= 0xc_780 and c <= 0xc_783)
        or (c >= 0xc_ab9 and c <= 0xc_bbf)
        or (c >= 0xc_c80 and c <= 0xc_da2)
    ):
     return True
   return False

  def checkuae(_snake_case     ):
   lowerCAmelCase    				= x.encode()
   if len(_snake_case     ) == 1 and len(_snake_case     ) == 3:
    lowerCAmelCase    				= (int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
    if c >= 0xe28_080 and c <= 0xe2b_07f:
     return True
   return False

  lowerCAmelCase    				= 0
  lowerCAmelCase    				= []
  while pos < len(_snake_case     ):
   lowerCAmelCase    				= min(len(_snake_case     )				,       pos + self.maxlen + 1     ) if text[pos] == '<' else pos + 3
   lowerCAmelCase    				= []  # (token_id, token, pos)
   for e in range(_snake_case				,       _snake_case				,       -1     ):
    lowerCAmelCase    				= text[pos:e]
    if wd in self.vocab:
     if wd[0] == "<" and len(_snake_case     ) > 2:
      lowerCAmelCase    				= [(self.vocab[wd], wd, e)]
      break
     else:
      candidates.append((self.vocab[wd], wd, e)     )
   if len(_snake_case     ) > 0:
    # the smallest token_id is adopted
    lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= sorted(_snake_case				,       key=lambda _snake_case     : x[0]     )[0]
    result.append(_snake_case     )
    lowerCAmelCase    				= e
   else:
    lowerCAmelCase    				= pos + 1
    lowerCAmelCase    				= text[pos:end]
    if check_simbol(_snake_case     ):
     result.append('<KIGOU>'     )
    elif checkuae(_snake_case     ):
     result.append('<U2000U2BFF>'     )
    else:
     for i in wd.encode('utf-8'     ):
      result.append('<|byte%d|>' % i     )
    lowerCAmelCase    				= end
  return result



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case="\n"     ):







  """simple docstring"""






  lowerCAmelCase    				= []
  lowerCAmelCase    				= []
  lowerCAmelCase    				= self.ids_to_tokens[index][0]
  if word[:6] == "<|byte" and word[-2:] == "|>":
   byte_tokens.append(int(word[6:-2]     )     )
  else:
   if len(_snake_case     ) > 0:
    words.append(bytearray(_snake_case     ).decode('utf-8'				,       errors='replace'     )     )
    lowerCAmelCase    				= []
   if word[:7] == "<|emoji" and word[-2:] == "|>":
    words.append(self.emoji['emoji_inv'][word]     )
   elif word == "<SP>":
    words.append(' '     )
   elif word == "<BR>":
    words.append(_snake_case     )
   elif word == "<TAB>":
    words.append('\t'     )
   elif word == "<BLOCK>":
    words.append('▀'     )
   elif word == "<KIGOU>":
    words.append('ǀ'     )
   elif word == "<U2000U2BFF>":
    words.append('‖'     )
   else:
    words.append(_snake_case     )
  if len(_snake_case     ) > 0:
   words.append(bytearray(_snake_case     ).decode('utf-8'				,       errors='replace'     )     )
  lowerCAmelCase    				= ''.join(_snake_case     )
  return text













"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records